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Spatiotemporal modeling and analysis of transient gene delivery
Author(s) -
Jandt Uwe,
Shao Shi,
Wirth Manfred,
Zeng AnPing
Publication year - 2011
Publication title -
biotechnology and bioengineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.136
H-Index - 189
eISSN - 1097-0290
pISSN - 0006-3592
DOI - 10.1002/bit.23175
Subject(s) - transfection , intracellular , transient (computer programming) , gene delivery , hek 293 cells , biological system , plasmid , biophysics , computational biology , cell culture , microbiology and biotechnology , biology , chemistry , gene , computer science , genetics , operating system
A quantitative and mechanistic understanding of intracellular transport processes in eukaroytic cells during transient transfection is an important prerequisite for the systematic and specific optimization of transient gene expression procedures for pharmaceutic and industrial protein production. There is evidence that intracellular transport processes during gene delivery and their regulation may have significant influence on the transfection efficiency. This contribution describes a compartmented, spatiotemporally resolved and stochastic modeling approach that describes intracellular transport processes responsible for gene delivery during transient transfection. It enables a detailed prediction and analysis and identification of potential bottlenecks. This model is currently being adapted to a model cell line, HEK293s. The simulated results are compared with experimental quantitative polymerase chain reaction (qPCR) data and confocal imaging data obtained with transfected and stained HEK293 cells. Global parameter estimation is performed to qPCR data based on two different novel plasmid constructs in order to identify candidates for plasmid‐specific transport parameter variations. The influence of the specific property of HEK293 cells to grow in clusters is investigated and the impact of active microtubule transport depending on cell morphology and clustering is examined. A general sensitivity analysis allows for the identification of the sensitive parameters. Biotechnol. Bioeng. 2011;108:2205–2217. © 2011 Wiley Periodicals, Inc.